Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	59,113
2	South Dakota	53,494
3	Iowa	41,551
4	Wisconsin	41,464
5	Mississippi	40,488
6	Louisiana	40,380
7	Alabama	39,563
8	Tennessee	37,729
9	Florida	37,592
10	Arkansas	37,463
11	Nebraska	37,048
12	Idaho	36,524
13	Utah	36,341
14	Georgia	35,196
15	South Carolina	34,576
16	Arizona	34,001
17	Illinois	33,527
18	Texas	33,008
19	Nevada	32,983
20	Oklahoma	31,365
21	Montana	31,339
22	Missouri	31,302
23	Rhode Island	31,031
24	Kansas	29,797
25	Indiana	27,418
26	New Jersey	27,180
27	Minnesota	26,726
28	New York	26,446
29	North Carolina	26,382
30	Delaware	25,802
31	Kentucky	24,755
32	District of Columbia	24,610
33	Maryland	24,253
34	California	23,761
35	Wyoming	23,711
36	Massachusetts	23,171
37	Alaska	22,687
38	New Mexico	22,525
39	Virginia	21,368
40	Puerto Rico	20,872
41	Connecticut	19,972
42	Michigan	19,776
43	Colorado	19,212
44	Ohio	18,735
45	Pennsylvania	16,755
46	Washington	14,853
47	West Virginia	13,884
48	Hawaii	10,796
49	Oregon	10,768
50	New Hampshire	8,247
51	Maine	4,995
52	Vermont	3,519

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,711
2	South Dakota	1,629
3	Wisconsin	842
4	Iowa	830
5	Montana	799
6	Wyoming	700
7	Illinois	611
8	Utah	607
9	Nebraska	605
10	Alaska	537
11	Minnesota	494
12	Indiana	465
13	Colorado	461
14	Missouri	395
15	Idaho	387
16	Kentucky	379
17	New Mexico	370
18	Arkansas	369
19	Kansas	339
20	Oklahoma	330
21	Alabama	328
22	Nevada	300
23	Ohio	286
24	West Virginia	263
25	Michigan	252
26	Tennessee	240
27	North Carolina	231
28	Arizona	227
29	South Carolina	222
30	Mississippi	214
31	Texas	212
32	Massachusetts	200
33	Florida	198
34	Delaware	196
35	Puerto Rico	194
36	New Jersey	190
37	Rhode Island	176
38	Pennsylvania	166
39	Virginia	164
40	Georgia	152
41	Maryland	151
42	District of Columbia	139
43	Oregon	128
44	New York	112
45	New Hampshire	109
46	Louisiana	107
47	Washington	103
48	California	101
49	Connecticut	71
50	Maine	61
51	Hawaii	56
52	Vermont	29

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,841
2	New York	1,705
3	Massachusetts	1,452
4	Connecticut	1,294
5	Louisiana	1,274
6	Rhode Island	1,133
7	Mississippi	1,124
8	District of Columbia	915
9	Arizona	821
10	Illinois	796
11	Florida	781
12	Michigan	771
13	South Carolina	764
14	Georgia	733
15	Delaware	729
16	North Dakota	703
17	Pennsylvania	694
18	Maryland	686
19	Arkansas	648
20	Indiana	648
21	Texas	640
22	Alabama	606
23	Nevada	578
24	Iowa	549
25	Missouri	503
26	South Dakota	493
27	New Mexico	489
28	Tennessee	486
29	Ohio	453
30	Minnesota	448
31	California	447
32	Virginia	428
33	North Carolina	420
34	Colorado	401
35	Wisconsin	362
36	New Hampshire	355
37	Idaho	353
38	Kansas	353
39	Montana	353
40	Kentucky	347
41	Nebraska	340
42	Oklahoma	339
43	Washington	324
44	Puerto Rico	260
45	West Virginia	255
46	Utah	191
47	Oregon	163
48	Hawaii	153
49	Wyoming	150
50	Maine	109
51	Alaska	106
52	Vermont	92

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	13
2	Montana	12
3	South Dakota	12
4	Arkansas	7
5	Delaware	7
6	Missouri	6
7	Indiana	5
8	New Mexico	5
9	Wisconsin	5
10	Alabama	4
11	Mississippi	4
12	Tennessee	4
13	Illinois	3
14	Minnesota	3
15	North Carolina	3
16	Oklahoma	3
17	South Carolina	3
18	Alaska	2
19	Arizona	2
20	Florida	2
21	Idaho	2
22	Iowa	2
23	Kansas	2
24	Kentucky	2
25	Massachusetts	2
26	Nebraska	2
27	Texas	2
28	West Virginia	2
29	Georgia	1
30	Louisiana	1
31	Maryland	1
32	Michigan	1
33	Nevada	1
34	Oregon	1
35	Pennsylvania	1
36	Puerto Rico	1
37	Rhode Island	1
38	Utah	1
39	California	0
40	Colorado	0
41	Connecticut	0
42	District of Columbia	0
43	Hawaii	0
44	Maine	0
45	New Hampshire	0
46	New Jersey	0
47	New York	0
48	Ohio	0
49	Vermont	0
50	Virginia	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	179,438	1	99
Chattahoochee	Georgia	167,415	2	99
Trousdale	Tennessee	159,075	3	99
Lafayette	Florida	151,864	4	99
Bon Homme	South Dakota	147,225	5	99
Davidson	Tennessee	50,393	321	89
Richland	South Carolina	42,462	533	83
York	South Carolina	24,913	1594	49
Orange	California	19,548	2048	34
Pierce	Washington	12,994	2547	18

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Jerauld	South Dakota	6,458	1	99
Emporia city	Virginia	5,238	2	99
Hancock	Georgia	5,203	3	99
Kenedy	Texas	4,950	4	99
Galax city	Virginia	4,727	5	99
Richland	South Carolina	659	892	71
Davidson	Tennessee	507	1211	61
Orange	California	467	1317	58
York	South Carolina	381	1561	50
Pierce	Washington	283	1860	40

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.